A method for evaluating the operating status of motors in automated production lines
Through the automated production line motor operating status evaluation method, motor operation data is collected and analyzed, abnormalities are identified and early warning is issued, which solves the production line shutdown caused by motor failure and ensures product quality and production efficiency.
Patent Information
- Application Number
- CN202210179282.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-02-25
- Publication Date
- 2024-12-27
- Estimated Expiration
- 2042-02-25
AI Technical Summary
In automated production lines, motor failures may cause the entire production line to be shut down, resulting in waste of raw materials and degraded product quality, and a lack of an effective early alarm mechanism for preventive repairs.
Using the automatic production line motor operating status evaluation method, through data acquisition, screening and system alarm, the motor operation data per second is recorded, the average value and standard deviation of the actual power and material flow of the motor are calculated, the constant is dynamically adjusted to identify abnormalities, and the warning is issued to prevent Fault.
It realizes timely alarms for motor failures, prevents production line shutdown, ensures product quality and production efficiency, and improves accurate judgment of production line operating status.
Smart Images
Figure CN114547900B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a motor evaluation method, in particular to a motor running state evaluation method for an automated production line, and belongs to the technical field of automated production. Background Art
[0002] In the manufacturing process of modern industrial products, automated production lines are widely used, which greatly improves production efficiency and ensures product quality. However, if any equipment on a key path fails during the use of automated production lines, the entire production line will be shut down, and it will cause a huge waste of raw materials and a serious decline in product quality. Because unexpected equipment failures have a huge impact on production, it is of great significance to prepare for possible early warnings for production line equipment and to issue timely alarms before possible failures. At present, the motors on the automated silk production line in tobacco factories are the most important power equipment on the production line. It is very necessary to accurately evaluate the operating status of the motors on the production line and perform preventive maintenance in advance. Summary of the invention
[0003] In order to ensure the normal operation of the motor of the power equipment of the automated silk production line in the tobacco factory, to be able to alarm before the motor fails and to perform preventive maintenance in advance, the present invention provides a method for evaluating the operating status of the motor of the automated production line.
[0004] The technical solution adopted by the present invention to solve the technical problem is: a method for evaluating the running status of motors in an automated production line, in which data collection, data screening and system alarm are performed in the stable production stage when all parts of the production unit have continuous materials, and the steps are as follows:
[0005] Step 1, Data Collection
[0006] Collect motor operation data X, and record the operation data X of each motor according to the sampling period per second i ;
[0007] Define batch S1 as a collection of motor operation data within a set time period. Where α1 is the total number of data in batch S1;
[0008] Sample data S is all batches S within the set period I r Data set, S = {S1, S2, S3...S r}, where r is the total number of batches in period I;
[0009] Determine multiple constants K based on the motor operation principle and historical data, and dynamically adjust according to the motor operation conditions;
[0010] Step 2: Batch data screening
[0011] Batch data screening is to sort the data by material batch based on the motor operation data collected in step 1, and calculate and analyze the absolute deviation of the motor actual power and the standard deviation of the motor actual power and the material flow of the production line, eliminate system abnormal data, and complete the preliminary screening of data;
[0012] Step 3: System alarm
[0013] The value calculated based on the data filtered in step 2 is compared with the corresponding constant K, and an alarm is issued when the alarm condition is met.
[0014] The data collection in step 1 is to record the motor operating status data at predetermined time intervals. The data recorded for each motor includes the process section code, production unit code, the work order number and batch number of the current produced by the production unit, the motor code, the current, power, and cumulative operating time of the motor in the process section, and the material flow of the production line.
[0015] The sample data is collected according to the set sampling period per second, and the collected sample data is separated according to the material batches. Taking batch S1 as an example, the motor power sample data of batch S1 is S1 = {P 31 , P 32 , ..., P 3n};
[0016] The sample data are calculated in batch order to calculate the average value of the actual power of the motor. The sample data in each batch are evaluated in turn according to the calculated average value. Taking batch S1 as an example, the average value of the actual power of the motor is calculated. and average material flow rate of production line
[0017]
[0018] In the formula, ——Average actual power of motors in batch S1
[0019] P 3n ——Actual power of motors in batch S1
[0020] a3——the total amount of data in the S1 batch
[0021]
[0022] In the formula, ——Average material flow rate of production line of S1 batch
[0023] Q 3n ——Material flow of S1 batch production line
[0024] Calculate the absolute deviation L3 between the average value of the actual power of the motors in batch S1 and the actual power of the motors.
[0025]
[0026] Where, L3 is the absolute deviation between the average actual power of the motor in batch S1 and the actual power of the motor
[0027] When L3 is greater than K 3-1 , it is abnormal data in the batch, which is marked and discarded, where K 3-1 2% of the rated power of the motor;
[0028] After that, continue to calculate the remaining batches until all batches are normal batches, and the data set of normal batches is defined as
[0029] When S T1 The total number of data in the S1 batch a3 is greater than the minimum original number K 3-2 When K 3-2 =100, calculate the standard deviation of the actual power of the motor σ 3-p , Standard deviation of production line material flow σ 3-q And the coefficient of variation Cv of the actual motor power 3-p , Coefficient of variation Cv of material flow of production line 3-q ,
[0030]
[0031] In the formula, σ 3-p ——Standard deviation of actual power of motors in batch S1
[0032]
[0033] In the formula, σ 3-q ——Standard deviation of material flow of S1 batch production line
[0034] Q 3n ——Material flow of S1 batch production line
[0035]
[0036] Where: Cv 3-p ——Coefficient of variation of actual power of S1 batch motors
[0037]
[0038] Where: Cv 3-q ——Coefficient of variation of material flow rate transported by S1 batch motor
[0039] Q 3n ——Material flow of S1 batch production line
[0040] When Cv 3-p Less than K 3-3 , Cv 3-q Less than K 3-4 When recording the batch statistics of the motor operation data, including the work order number, batch number, motor code, and the average value of the actual motor power and standard deviation σ 3-p , the average current, the maximum value of the motor's cumulative running time, the average value of the motor's material flow rate, and the standard deviation of the material flow rate σ 3-q , where K 3-3 =0.01, K 3-4 =0.02.
[0041] In the set cycle II of the motor operation, the batch set S is obtained. T1 , defined as the initial batch data set of the operation; calculate the maximum value of the accumulated running time of the motor t 4max ,
[0042] t 4max =max{t 4-1 ......t 4-r} (8)
[0043] In the formula, t 4max --S T1 The maximum cumulative running time of the motor in all batches
[0044] r--set S T1 Total number of batches within
[0045] When t 4max Less than the initial running hours of the motor K 4-1 When K 4-1 = 100 hours, the batch set is defined as For S T2 Calculate the average actual power of the motor
[0046]
[0047] Where: ——S T2 The average actual power of all batches of motors
[0048] a 4-1 ——S T2 Number of batches within
[0049] ——ST2 The average actual power of each batch of motors
[0050] According to the calculation Evaluation S T2 The average actual power of each batch of motors is S2 (S2∈S T2 ) batch as an example, calculate the average actual power of the motor
[0051]
[0052] In the formula, a 4-2 ——Number of actual power data of S2 batch motors
[0053] P 4n ——Actual power of motors in batch S2
[0054] Calculate the data set S T2 The average actual power of all batches of motors in S T2 The absolute deviation L4 of the average value of the actual power of each batch of motors,
[0055]
[0056] When L4 is greater than K 4-2 When , it is an abnormal batch, and the abnormal batch is marked and discarded. 4-2 2% of the rated power of the motor;
[0057] After that, continue to recalculate the remaining batches until all batches are normal, and the batch set of normal data is defined as
[0058] When S T3 The total number of batches in a 4-1 Greater than the total number of minimum batches, where K 4-3 When K 4-3 For 10 batches, calculate S T3 The standard deviation σ 4_p and coefficient of variation Cv 4_p ,
[0059]
[0060] Where: a 4-1 ——S T3 Number of batches
[0061] σ 4_p ——S T3 The standard deviation of the actual power of each batch of motors
[0062]
[0063] Where: Cv 4_p ——S T3 The coefficient of variation of the actual power of each batch of motors
[0064] When Cv 4_p Less than K 4-4 When , the set of batch data is defined as Where K 4-4 =0.01;
[0065] Record the sample data set S of the motor in the set period III T4 , including motor code, average value and standard deviation of actual motor power, average current value, maximum value of motor cumulative running time, etc.
[0066] According to the motor data during stable production, calculate the motor within the set period IV to obtain the data set of the normal batch Defined as running the recent batch data set, calculating the maximum cumulative running time t of the motor 5max ,
[0067] t 5max =max{t 5-1 ......t 5-r} (14)
[0068] Where: t 5max ——The maximum value of the motor’s cumulative running time
[0069] r——Number of data
[0070] When t 5max Greater than the current motor cumulative running time t total Subtract the preset motor recent running time K 5-1 , where K 5-1 = 100 hours, the batch set is defined as For S T2 'Calculate the average actual power of the motor
[0071]
[0072] Where: ——S T1 The average value of the actual power of all batches of motors in
[0073] a 5-1 ——S T2 'Total number of batches
[0074] according to Evaluating the average power of S3 batches Take S3(S3∈S T2 ') batch as an example, calculate the actual power average value of the motor
[0075]
[0076] Where: ——Average power of the actual power of the S3 batch of motors
[0077] P 5n ——Actual power of S3 batch motors
[0078] a 5-2 ——Number of actual power data of motors in S3 batch
[0079] Calculate the dataset T2 The average actual power of all batches of motors in S T2 The absolute deviation L5 of the average value of the actual power of each batch of motors in '
[0080]
[0081] Where: L5——S T2 The absolute deviation between the average value of the actual power of all batches of motors in ' and the average power of the actual power of the motors in batch S3,
[0082] When L5 is greater than K 5-2 When , it is an abnormal batch, and the batch is marked and discarded, where K 5-2 =0.01;
[0083] After that, continue to recalculate the remaining batches until all batches are normal batches; the set of normal batch data is defined as
[0084] When S T3 'The total number of batches in a 5-1 Greater than the minimum batch size K 5-3 When K 5-3 = 10 batches, calculate the standard deviation σ of the actual power of all normal batches of motors 5-p and coefficient of variation Cv 5-p ,
[0085]
[0086] Where: 5-p ——S T3 'Standard deviation of actual power within a batch
[0087] a 5-1 ——S T3 'Number of batches
[0088]
[0089] Where: Cv 5-p ——S T3 'Coefficient of variation of actual power within a batch
[0090] When Cv 5-p Less than K 5-4 When K 5-4 = 0.01, the set of batch data is defined as
[0091] Record the sample data set S of the motor within the set period V T4 ', including the motor code, the average and standard deviation of the actual motor power, the average current, and the maximum value of the motor's accumulated running time.
[0092] In the stable production stage, the motor data is counted and the average actual power of a random batch of motors within the set period VI is calculated. Take batch S4 as an example.
[0093]
[0094] Where: ——Average actual power of motors in batch S4
[0095] P 6n ——Actual power of the S4 batch of motors
[0096] a6——The number of data collected in the S4 batch
[0097] Calculate the average actual power of the motors in batch S4 and S T2 The absolute deviation of the average actual power of all batches of motors in L6,
[0098]
[0099] Where: ——S T2 The average actual power of all batches of motors
[0100] The average actual power of the motors in batches L6-S4 and S T2 The difference between the average actual power of all batches of motors within
[0101] When L6 is greater than K6, it is determined that the initial state of the motor operation is poor, and an alarm for the initial state of the motor operation is issued. K6 is 2% of the rated power of the motor.
[0102] Statistics of sample data sets running within the set period VII The average power With S T2 The average actual power of all batches of motors in The absolute deviation L7,
[0103]
[0104] Where: ——S T2 The average actual power of all batches of motors in
[0105] ——Set the sample data set S running in period VII T4 The average power
[0106] When L7 is greater than K7, it is determined that the motor is in a poor operating state and an alarm for a poor motor operating state is issued. K7 is 2% of the rated power of the motor.
[0107] The motors are sorted in the direction of material flow in the production unit. During the stable production stage, the motor operation data is collected at a sampling period of 1 s. Sample data {P 81 , P 82 , ..., P 8m}, where m = t8, calculate the average value of the sample data
[0108]
[0109] Where: ——Average value of actual motor power during time t8
[0110] P 8m ——Actual power of the motor during time t8
[0111] Calculate the absolute deviation L between the average actual power of the motor during time t8 and the actual power during time t8 8-1 ,
[0112]
[0113] Where: L 8-1 ——The absolute deviation between the average actual power of the motor during time t8 and the actual power during time t8
[0114] When P 8m and The absolute deviation L 8-1 When it is greater than K8, the motor is deemed to be in poor operating condition and an alarm for poor motor operating condition is issued. K8 is 2% of the rated power of the motor.
[0115] When L 8-1 P of the motor adjacent to the production sequence 8(m-1) With P 8(m+1) The absolute deviation L 8-2 , L 8-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
[0116] The motors are sorted in the direction of material flow in the production unit. During the stable production stage, the motor operation data is collected at a sampling period of 1 s. Sample data {P 91 , P 92 , ..., P 9m}, m = t9, calculate the average value of sample data
[0117]
[0118] Where: P 9m ——Actual power of the motor during time t9
[0119] ——Average value of actual motor power during time t9
[0120] Calculate the absolute deviation L between the average actual power of the motor during time t9 and the actual power during time t9 9-1 ,
[0121]
[0122] Where: L 9-1 ——The absolute deviation between the average actual power of the motor during time t9 and the actual power during time t9
[0123] When L 9-1 When it is greater than K9, K9=0.01, it is determined that the motor is in a bad operating state and an alarm for the bad motor operating state is issued;
[0124] When L9 is adjacent to the P 9(m+1) With P 9(m-1) The absolute deviation L 9-2 , L 9-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
[0125] Furthermore, for the motor belt, when the actual power of the motor suddenly decreases, it is considered that the belt is slipping; when the actual power of the motor suddenly increases, it is considered that the belt is blocked.
[0126] The beneficial effect of the present invention is that, based on a large amount of collected production line motor data, it has an alarm function for faults that occur during the long-term operation of the motor, thereby preventing the occurrence of production line shutdowns, material cuts and other faults due to sudden motor failures, ensuring the timely completion of production plans, ensuring the stability of product quality, improving production efficiency, and achieving accurate judgment of the operating status of the production line. BRIEF DESCRIPTION OF THE DRAWINGS
[0127] Figure 1 This is a diagram of the data analysis method within the same batch;
[0128] Figure 2 This is a diagram of the data analysis method between different batches of the initial operation of the motor;
[0129] Figure 3 This is a diagram of the data analysis method between different batches of recent motor operations;
[0130] Figure 4 This is the diagram of the alarm method for the initial operation of the motor;
[0131] Figure 5 This is a diagram of the recent operation alarm method of the motor;
[0132] Figure 6 This is the diagram of the motor t8 second alarm method;
[0133] Figure 7 This is a diagram of the motor t9 second alarm method. DETAILED DESCRIPTION
[0134] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. However, those skilled in the art should be aware that the present invention is not limited to the specific embodiments listed, and all embodiments should be included in the protection scope of the present invention as long as they conform to the spirit of the present invention.
[0135] The automated silk-making production line in a tobacco factory includes a number of sequentially arranged conveying equipment. The production materials enter the conveying equipment in sequence for processing in different production units, which includes the following stages: the stage from when the production materials enter the first conveying equipment to when they are sent out of the last conveying equipment is called the initial stage; after the initial stage, each part of the production unit has continuous materials, and this stage is called the stable production stage. When all the materials in this batch leave the first conveying equipment, it marks the end of the stable production stage, until all the materials leave the last equipment, this stage is called the ending stage.
[0136] The material batch data set includes: an initial batch data set and a recent batch data set. The initial batch data set refers to the collection of all batches for 7 consecutive days starting from the statistics of the motor operation data; the recent batch data set refers to the collection of all batches for 14 consecutive days starting from the statistics of the motor operation data.
[0137] The method for evaluating the operating status of motors in automated production lines of the present invention collects, records, counts and analyzes a large amount of data on production units and motor equipment of the production line, and timely issues alarms for possible faults of motors in the production line based on the status of the production units of the production line, initial batch data sets of the motors, recent batch data sets, and current operating data. The method specifically includes: automatic recording of production line motor data, statistical analysis of motor data, and alarms for motor operating status.
[0138] See attached Figure 1-7 The present invention provides a method for evaluating the running status of motors in an automated production line. In the stable production stage when all parts of the production unit have continuous materials, data collection, data screening and system alarm are performed. The steps are as follows:
[0139] Step 1, Data Collection
[0140] Collect motor operation data X, and record the operation data X of each motor according to the sampling period per second i .
[0141] Define batch S1 as a collection of motor operation data within a set time period, S1 = {X1, X2, X3...X α1}, where α1 is the total number of data in batch S1; the certain time period is the continuous running time of the batch from the initial stage to the end stage.
[0142] Sample data S is all batches S within the set period I r Data set, S = {S1, S2, S3...S r}, where: r is the total number of batches in the set period; period I is set to 30 days, that is, 30 consecutive days from the start of motor operation data statistics.
[0143] A plurality of constants K are determined according to the motor operation principle and historical data, and are dynamically adjusted according to the motor operation conditions. The subscript of the specific constant K is the identifier of each calculation process.
[0144] Step 2: Batch data screening
[0145] Data screening is based on the motor operation data collected in step 1. The data is sorted by material batch, and the absolute deviation of the actual motor power and the standard deviation of the actual motor power and the material flow of the production line are calculated and analyzed. Abnormal system data is eliminated to complete the preliminary screening of the data.
[0146] Step 3: System alarm
[0147] The value obtained by calculating the data filtered in step 2 is compared with the corresponding constant K, and an alarm is issued when the alarm condition is met.
[0148] The system alarm is divided according to the operation cycle, using a batch comparison alarm method or an adjacent material comparison alarm method.
[0149] The data collection in step 1 is to record the motor operating status data at predetermined time intervals. The data recorded for each motor includes the process section code, the production unit code, the work order number and batch number of the current produced by the production unit, the motor code, the current, power, and cumulative operating time of the motor in the process section; while recording the motor operating status data, the material flow of the production line is recorded.
[0150] See attached Figure 1-3 , the batch data screening of step 2 is as follows:
[0151] See attached Figure 1 The sample data is collected according to the set sampling period per second, and the collected sample data is separated according to the material batches. Taking batch S1 as an example, the motor power sample data of batch S1 is S1 = {P 31 , P 32 , ..., P 3n}.
[0152] The sample data are calculated in batch order to calculate the average value of the actual power of the motor. The sample data in each batch are evaluated in turn according to the calculated average value. Taking batch S1 as an example, the average value of the actual power of the motor is calculated. and average material flow rate of production line
[0153]
[0154] In the formula, ——Average actual power of motors in batch S1
[0155] P 3n ——Actual power of motors in batch S1
[0156] a3——the total amount of data in the S1 batch
[0157]
[0158] In the formula, ——Average material flow rate of production line of S1 batch
[0159] Q 3n ——Material flow of S1 batch production line
[0160] Calculate the absolute deviation L3 between the average value of the actual power of the motors in batch S1 and the actual power of the motors.
[0161]
[0162] Where, L3 is the absolute deviation between the average actual power of the motor in batch S1 and the actual power of the motor
[0163] When L3 is greater than K 3-1 , it is abnormal data in the batch, which is marked and discarded, where K 3-1 2% of the rated power of the motor.
[0164] After that, continue to calculate the remaining batches until all batches are normal batches, and the data set of normal batches is defined as
[0165] When S T1 The total number of data in the S1 batch a3 is greater than the minimum original number K 3-2 When K 3-2 =100, calculate the standard deviation of the actual power of the motor σ 3-p , Standard deviation of production line material flow σ 3-q And the coefficient of variation Cv of the actual motor power 3-p , Coefficient of variation Cv of material flow of production line 3-q ,
[0166]
[0167] In the formula, σ 3-p ——Standard deviation of actual power of motors in batch S1
[0168]
[0169] In the formula, σ 3-q ——Standard deviation of material flow of S1 batch production line
[0170] Q 3n ——Material flow of S1 batch production line
[0171]
[0172] Where, Cv 3-p ——Coefficient of variation of actual power of S1 batch motors
[0173]
[0174] Where: Cv 3-q ——Coefficient of variation of material flow rate transported by S1 batch motor
[0175] Q3n ——Material flow of S1 batch production line
[0176] When Cv 3-p Less than K 3-3 , Cv 3-q Less than K 3-4 When recording the batch statistics of the motor operation data, including the work order number, batch number, motor code, and the average value of the actual motor power and standard deviation σ 3-p , the average current, the maximum value of the motor's cumulative running time, the average value of the motor's material flow rate, and the standard deviation of the material flow rate σ 3-q , where K 3-3 =0.01, K 3-4 =0.02.
[0177] See attached Figure 2 In the set period II of the motor operation (period II is set to 7 days), the batch set S is obtained T1 , defined as the initial batch data set of operation, and period II is 7 consecutive days from the start of motor operation data statistics. Calculate the maximum cumulative running time t of the motor 4max ,
[0178] t 4max =max{t 4-1 ......t 4-r} (8)
[0179] In the formula, t 4max --S T1 The maximum cumulative running time of the motor in all batches
[0180] r--set S T1 Total number of batches within
[0181] When t 4max Less than the initial running hours of the motor K 4-1 When K 4-1 = 100 hours, the batch set is defined as For S T2 Calculate the average actual power of the motor
[0182]
[0183] Where: ——S T2 The average actual power of all batches of motors
[0184] a 4-1 ——S T2 Number of batches within
[0185] ——S T2 The average actual power of each batch of motors
[0186] According to the calculation Evaluation S T2 The average actual power of each batch of motors is S2 (S2∈S T2 ) batch as an example, calculate the average actual power of the motor
[0187]
[0188] In the formula, a 4-2 ——Number of actual power data of S2 batch motors
[0189] P 4n ——Actual power of motors in batch S2
[0190] Calculate the data set S T2 The average actual power of all batches of motors in S T2 The absolute deviation L4 of the average value of the actual power of each batch of motors,
[0191]
[0192] When L4 is greater than K 4-2 When , it is an abnormal batch, and the abnormal batch is marked and discarded. 4-2 2% of the rated power of the motor.
[0193] After that, continue to recalculate the remaining batches until all batches are normal, and the batch set of normal data is defined as
[0194] When S T3 The total number of batches in a 4-1 Greater than the total number of minimum batches, where K 4-3 When K 4-3 For 10 batches, calculate S T3 The standard deviation σ 4_p and coefficient of variation Cv 4_p ,
[0195]
[0196] Where: a 4-1 ——S T3 Number of batches
[0197] σ 4_p ——S T3 The standard deviation of the actual power of each batch of motors
[0198]
[0199] Where: Cv 4_p ——S T3 The coefficient of variation of the actual power of each batch of motors
[0200] When Cv 4_p Less than K 4-4 When the set of batch data is defined as S T4 Where K 4-4 =0.01;
[0201] The sample data set S records the motor in the set period III (period III is set to 7 days) T4 , including motor code, average value and standard deviation of actual motor power, average current value, maximum value of motor cumulative running time, etc.
[0202] See attached Figure 3 According to the motor data during stable production, the motor is calculated within the set period IV (period IV is set to 14 days) to obtain the data set of the normal batch Defined as running the recent batch data set, calculating the maximum cumulative running time t of the motor 5max ,
[0203] t 5max =max{t 5-1 ......t 5-r}(14)
[0204] Where: t 5max ——The maximum value of the motor’s cumulative running time
[0205] r——Number of data
[0206] When t 5max Greater than the current motor cumulative running time t total Subtract the preset motor recent running time K 5-1 , where K 5-1 = 100 hours, the batch set is defined as For S T2 'Calculate the average actual power of the motor
[0207]
[0208] Where: ——S T1 The average value of the actual power of all batches of motors in
[0209] a 5-1——S T2 'Total number of batches
[0210] according to Evaluating the average power of S3 batches Take S3(S3∈S T2 ') batch as an example, calculate the actual power average value of the motor
[0211]
[0212] Where: ——Average power of the actual power of the S3 batch of motors
[0213] P 5n ——Actual power of S3 batch motors
[0214] a 5-2 ——Number of actual power data of motors in S3 batch
[0215] Calculate the data set S T2 The average actual power of all batches of motors in S T2 The absolute deviation L5 of the average value of the actual power of each batch of motors in '
[0216]
[0217] Where: L5——S T2 The absolute deviation between the average value of the actual power of all batches of motors in ' and the average power of the actual power of the motors in batch S3,
[0218] When L5 is greater than K 5-2 When , it is an abnormal batch, and the batch is marked and discarded, where K 5-2 =0.01.
[0219] After that, continue to recalculate the remaining batches until all batches are normal batches; the set of normal batch data is defined as
[0220] When S T3 'The total number of batches in a 5-1 Greater than the minimum batch size K 5-3 When K 5-3 = 10 batches, calculate the standard deviation σ of the actual power of all normal batches of motors 5-p and coefficient of variation Cv 5-p ,
[0221]
[0222] Where: 5-p ——S T3'Standard deviation of actual power within a batch
[0223] a 5-1 ——S T3 'Number of batches
[0224]
[0225] Where: Cv 5-p ——S T3 'Coefficient of variation of actual power within a batch
[0226] When Cv 5-p Less than K 5-4 When K 5-4 = 0.01, the set of batch data is defined as
[0227] The sample data set S records the motor in the set period V (period V is set to 14 days) T4 ', including the motor code, the average and standard deviation of the actual motor power, the average current, and the maximum value of the motor's accumulated running time.
[0228] See attached Figure 4-7 The system alarm of step 3 includes a batch comparison alarm method and an adjacent material comparison alarm method, which are as follows:
[0229] Batch comparison alarm method Ⅰ, as attached Figure 4 In the stable production stage, the motor data is statistically analyzed to calculate the average actual power of a random batch of motors within the set period VI (period VI is set to 7 days). Take batch S4 as an example.
[0230]
[0231] Where: ——Average actual power of motors in batch S4
[0232] P 6n ——Actual power of the S4 batch of motors
[0233] a6——The number of data collected in the S4 batch
[0234] Calculate the average actual power of the motors in batch S4 and S T2 The absolute deviation of the average actual power of all batches of motors in L6
[0235]
[0236] Where: ——S T2The average actual power of all batches of motors
[0237] The average actual power of the motors in batches L6-S4 and S T2 The difference between the average actual power of all batches of motors within
[0238] When L6 is greater than K6, it is determined that the initial state of the motor operation is poor, and an alarm for the initial state of the motor operation is issued. K6 is 2% of the rated power of the motor.
[0239] Batch comparison alarm method II, as attached Figure 5 As shown. The sample data set S that runs within the set period VII (period VII is set to 14 days) is counted T4 ' The average power With S T2 The average actual power of all batches of motors in The absolute deviation L7,
[0240]
[0241] Where: ——S T2 The average actual power of all batches of motors in
[0242] ——The sample data set S running in period VII T4 The average power
[0243] When L7 is greater than K7, it is determined that the motor is in a poor operating state and an alarm for a poor motor operating state is issued. K7 is 2% of the rated power of the motor.
[0244] Adjacent material comparison alarm method Ⅰ, as attached Figure 6 In the production unit, the motors are sorted in the direction of material flow. In the stable production stage, the motor operation data is collected according to the 1s sampling period. The sampling time is t8, and t8 is the continuous sampling time from the start of the motor operation at any time. The sample data {P 81 , P 82 , ..., P 8m}, m = t8, calculate the average value of sample data
[0245]
[0246] Where: ——Average value of actual motor power during time t8
[0247] P 8m ——Actual power of the motor during time t8
[0248] Calculate the absolute deviation L between the average actual power of the motor during time t8 and the actual power during time t8 8-1 ,
[0249]
[0250] Where: L 8-1 ——The absolute deviation between the average actual power of the motor during time t8 and the actual power during time t8
[0251] When P 8m and The absolute deviation L 8-1 When it is greater than K8, the motor is deemed to be in poor operating condition and an alarm for poor motor operating condition is issued. K8 is 2% of the rated power of the motor.
[0252] When L 8-1 P of the motor adjacent to the production sequence 8(m-1) With P 8(m+1) The absolute deviation L 8-2 , L 8-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
[0253] Adjacent material comparison alarm method II, as attached Figure 7 The motors are sorted in the direction of material flow in the production unit. In the stable production stage, the motor operation data is collected according to the 1s sampling period. The sampling time is t9, where t9 is the continuous sampling time from the start of the motor operation at any time. The sample data {P 91 , P 92 , ..., P 9m}, m = t9, calculate the average value of sample data
[0254]
[0255] Where: P 9m ——Actual power of the motor during time t9
[0256] ——Average value of actual motor power during time t9
[0257] Calculate the absolute deviation L between the average actual power of the motor during time t9 and the actual power during time t9 9-1 ,
[0258]
[0259] Where: L 9-1——The absolute deviation between the average actual power of the motor during time t9 and the actual power during time t9
[0260] When L 9-1 When it is greater than K9, K9=0.01, it is determined that the motor is in a poor operating state and an alarm for the poor operating state of the motor is issued.
[0261] When L9 is adjacent to the P 9(m+1) With P 9(m-1) The absolute deviation L 9-2 , L 9-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
[0262] When judging that the motor is in a bad operating state, for the motor belt, when the actual power of the motor suddenly decreases, it is considered that the belt is slipping; when the actual power of the motor suddenly increases, it is considered that the belt is blocked.
[0263] The present invention discloses an automatic production line motor running state evaluation method. The control of the production line motor is completed by a programmable controller PLC, data collection is completed by a computer workstation and a server, and the computer system and the PLC are connected and communicated by Ethernet. The PLC generates a production unit state after judging the actual production operation status, obtains the motor current data by using the measurement and control device of the motor operation, and calculates the motor real-time power by using the voltage data of the electric control cabinet where the PLC is located, and obtains the motor cumulative running time by accumulating the running time. The data center program on the computer workstation or server completes the processing of the relevant motor running data through socket communication and OPC and other data collection and distribution methods. After the relevant motor replacement and maintenance work is completed, the motor cumulative running time is cleared; at the same time, the data center software pushes the motor running data to the relevant big data storage service and motor data real-time processing service through the distributed message queue Kafka. The big data storage service is responsible for saving the motor data in the hbase database in a pre-designed form. The motor data real-time processing service uses the redis distributed memory database to save the motor data and calculation results required for real-time calculation, and uses mapreduce and other data real-time calculation methods and tools to complete real-time calculation and related alarms. The equipment management service arranges the operation and maintenance work of the relevant motors in a timely manner according to the alarm. The front-end application obtains the relevant work arrangements and alarm notifications to the relevant users in a timely manner.
[0264] It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the claims of this patent.
Claims
1. A method for evaluating the operating status of a motor in an automated production line, characterized in that: Each part of the production unit has a stable production stage of continuous materials, data collection, data screening and system alarm. The steps are as follows: Step 1, Data Collection Collect motor operation data X, and record the operation data X of each motor according to the sampling period per second i ; Define batch S1 as a collection of motor operation data within a set time period, S1 = {X1, X2, X3...X α1 }, where α1 is the total number of data in batch S1; Sample data S is the data set of all batches Sr within the set period I, S = {S1, S2, S3...S r }, where r is the total number of batches in period I; Determine multiple constants K based on the motor operation principle and historical data, and dynamically adjust according to the motor operation conditions; Step 2: Batch data screening Batch data screening is to sort the data by material batch based on the motor operation data collected in step 1, and calculate and analyze the absolute deviation of the motor actual power and the standard deviation of the motor actual power and the material flow of the production line, eliminate system abnormal data, and complete the preliminary screening of data; Step 3: System alarm The value calculated based on the data filtered in step 2 is compared with the corresponding constant K, and an alarm is issued when the alarm condition is met; The data acquisition in step 1 is to record the motor operation status data at a predetermined time interval, and the data recorded for each motor includes the process section code, the production unit code, the work order number, batch number, motor code, the current, power, and cumulative operation time of the motor in the process section, and the material flow of the production line; The sample data is collected according to the set sampling period per second, and the collected sample data is separated according to the material batch. Taking batch S1 as an example, the motor power sample data of batch S1 is S1={P 31 , P 32 , ..., P 3n }; The sample data are calculated in batch order to calculate the average value of the actual power of the motor. The sample data in each batch are evaluated in turn according to the calculated average value. Taking batch S1 as an example, the average value of the actual power of the motor is calculated. and average material flow rate of production line In the formula, ——Average actual power of motors in batch S1 P 3n ——Actual power of motors in batch S1 a3——the total amount of data in the S1 batch In the formula, ——Average material flow rate of production line of S1 batch Q 3n ——Material flow of S1 batch production line Calculate the absolute deviation L3 between the average value of the actual power of the motors in batch S1 and the actual power of the motors. Where, L3 is the absolute deviation between the average actual power of the motor in batch S1 and the actual power of the motor When L3 is greater than K 3-1 , it is abnormal data in the batch, which is marked and discarded, where K 3-1 2% of the rated power of the motor; After that, continue to calculate the remaining batches until all batches are normal batches, and the data set of normal batches is defined as When S T1 The total number of data in the S1 batch a3 is greater than the minimum original number K 3-2 When K 3-2 =100, calculate the standard deviation of the actual power of the motor σ 3-p , Standard deviation of production line material flow σ 3-q And the coefficient of variation Cv of the actual motor power 3-p , Coefficient of variation Cv of material flow of production line 3-q , In the formula, σ 3-p ——Standard deviation of actual power of motors in batch S1 In the formula, σ 3-q ——Standard deviation of material flow of S1 batch production line Where: Cv 3-p ——Coefficient of variation of actual power of S1 batch motors Where: Cv 3-q ——Coefficient of variation of material flow rate transported by S1 batch motor When Cv 3-p Less than K 3-3 , Cv 3-q Less than K 3-4 When recording the batch statistics of the motor operation data, including the work order number, batch number, motor code, and the average value of the actual motor power and standard deviation σ 3-p , the average current, the maximum value of the motor's cumulative running time, the average value of the motor's material flow rate, and the standard deviation of the material flow rate σ 3-q , where K 3-3 =0.01, K 3-4 =0.
02.
2. The method for evaluating the operating status of an electric motor in an automated production line according to claim 1 is characterized in that: exist Within the set period II of the motor operation, the batch set S is obtained T1 , defined as the initial batch data set of the operation; calculate the maximum value of the accumulated running time of the motor t 4max , t 4max =max{t 4-1 ......t 4-r } (8) Where, t 4max --S T1 The maximum cumulative running time of the motor in all batches r--set S T1 Total number of batches within When t 4max Less than the initial running hours of the motor K 4-1 When K 4-1 = 100 hours, the batch set is defined as For S T2 Calculate the actual average power of the motor X4, Where: ——S T2 The average actual power of all batches of motors a 4-1 ——S T2 Number of batches within ——S T2 The average actual power of each batch of motors According to the calculation Evaluation S T2 The actual power average of each batch of motors is S2, S2∈S T2 Taking batch as an example, calculate the average actual power of the motor In the formula, a 4-2 ——Number of actual power data of S2 batch motors P 4n ——Actual power of motors in batch S2 Calculate the data set S T2 The average actual power of all batches of motors in S T2 The absolute deviation L4 of the average value of the actual power of each batch of motors, When L4 is greater than K 4-2 When , it is an abnormal batch, and the abnormal batch is marked and discarded. 4-2 2% of the rated power of the motor; After that, continue to recalculate the remaining batches until all batches are normal, and the batch set of normal data is defined as When S T3 The total number of batches in a 4-1 Greater than the total number of minimum batches, where K 4-3 When K 4-3 For 10 batches, calculate S T3 The standard deviation σ 4_p and coefficient of variation Cv 4_p , Where: a 4-1 ——S T3 Number of batches σ 4_p ——S T3 The standard deviation of the actual power of each batch of motors Where: Cv 4_p ——S T3 The coefficient of variation of the actual power of each batch of motors When Cv 4_p Less than K 4-4 When , the set of batch data is defined as Where K 4-4 =0.01; Record the sample data set S of the motor in the set period III T4 , including motor code, average value and standard deviation of actual motor power, average current value, maximum value of motor cumulative running time, etc.
3. The method for evaluating the operating status of an electric motor in an automated production line according to claim 2 is characterized in that: According to the motor data during stable production, calculate the motor within the set period IV to obtain the data set of the normal batch Defined as running the recent batch data set, calculating the maximum cumulative running time t of the motor 5max , t 5max =max{t 5-1 ……t 5-r } (14) Where: t 5max ——The maximum value of the motor’s cumulative running time r——Number of data When t 5max Greater than the current motor cumulative running time t total Subtract the preset motor recent running time K 5-1 , where K 5-1 = 100 hours, the batch set is defined as For S T2 'Calculate the average actual power of the motor Where: ——S T1 The average value of the actual power of all batches of motors in a 5-1 ——S T2 'Total number of batches according to Evaluating the average power of S3 batches Take S3, S3∈S T2 'Batch as an example, calculate the actual power average of the motor Where: ——Average power of the actual power of the S3 batch of motors P 5n ——Actual power of S3 batch motors a 5-2 ——Number of actual power data of motors in S3 batch Calculate the dataset T2 The average actual power of all batches of motors in S T2 The absolute deviation L5 of the average value of the actual power of each batch of motors in ' Where: L5——S T2 The absolute deviation between the average value of the actual power of all batches of motors in ' and the average power of the actual power of the motors in batch S3, When L5 is greater than K 5-2 When , it is an abnormal batch, and the batch is marked and discarded, where K 5-2 =0.01; After that, continue to recalculate the remaining batches until all batches are normal batches; the set of normal batch data is defined as When S T3 'The total number of batches in a 5-1 Greater than the minimum batch size K 5-3 When K 5-3 = 10 batches, calculate the standard deviation σ of the actual power of all normal batches of motors 5-p and coefficient of variation Cv 5-p , Where: 5-p ——S T3 'Standard deviation of actual power within a batch a 5-1 ——S T3 'Number of batches Where: Cv 5-p ——S T3 'Coefficient of variation of actual power within a batch When Cv 5-p Less than K 5-4 When K 5-4 = 0.01, the set of batch data is defined as Record the sample data set S of the motor within the set period V T4 ', including the motor code, the average and standard deviation of the actual motor power, the average current, and the maximum value of the motor's accumulated running time.
4. The method for evaluating the operating status of an electric motor in an automated production line according to claim 2 is characterized in that: In the stable production stage, the motor data is counted and the average actual power of a random batch of motors within the set period VI is calculated. Take batch S4 as an example. Where: ——Average actual power of motors in batch S4 P 6n ——Actual power of the S4 batch of motors a6——The number of data collected in the S4 batch Calculate the average actual power of the motors in batch S4 and S T2 The absolute deviation of the average actual power of all batches of motors in L6, Where: ——S T2 The average actual power of all batches of motors The average actual power of the motors in batches L6-S4 and S T2 The difference between the average actual power of all batches of motors within When L6 is greater than K6, it is determined that the initial state of the motor operation is poor, and an alarm for the initial state of the motor operation is issued. K6 is 2% of the rated power of the motor.
5. The method for evaluating the operating status of motors in an automated production line according to claim 3 is characterized by: Statistics of sample data sets running within the set period VII The average power With S T2 The average actual power of all batches of motors in The absolute deviation L7, Where: ——S T2 The average actual power of all batches of motors in ——Set the sample data set S running in period VII T4 The average power When L7 is greater than K7, it is determined that the motor is in a poor operating state and an alarm for a poor motor operating state is issued. K7 is 2% of the rated power of the motor.
6. The method for evaluating the operating status of motors in an automated production line according to claim 3 is characterized by: The motors are sorted in the direction of material flow in the production unit. During the stable production stage, the motor operation data is collected at a sampling period of 1 s. Sample data {P 81 , P 82 , ..., P 8m }, where m = t8, calculate the average value of the sample data Where: ——Average value of actual motor power during time t8 P 8m ——Actual power of the motor during time t8 Calculate the absolute deviation L between the average actual power of the motor during time t8 and the actual power during time t8 8-1 , Where: L 8-1 ——The absolute deviation between the average actual power of the motor during time t8 and the actual power during time t8 When P 8m and The absolute deviation L 8-1 When it is greater than K8, the motor is deemed to be in poor operating condition and an alarm for poor motor operating condition is issued. K8 is 2% of the rated power of the motor. When L 8-1 P of the motor adjacent to the production sequence 8(m-1) With P 8(m+1) The absolute deviation L 8-2 , L 8-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
7. The method for evaluating the operating status of motors in an automated production line according to claim 3 is characterized by: The motors are sorted in the direction of material flow in the production unit. During the stable production stage, the motor operation data is collected at a sampling period of 1 s. Sample data {P 91 , P 92 , ..., P 9m }, m = t9, calculate the average value of sample data Where: P 9m ——Actual power of the motor during time t9 ——Average value of actual motor power during time t9 Calculate the absolute deviation L between the average actual power of the motor during time t9 and the actual power during time t9 9-1 , Where: L 9-1 ——The absolute deviation between the average actual power of the motor during time t9 and the actual power during time t9 When L 9-1 When it is greater than K9, K9=0.01, it is determined that the motor is in a bad operating state and an alarm for the bad motor operating state is issued; When L9 is adjacent to the P 9(m+1) With P 9(m-1) The absolute deviation L 9-2 , L 9-3 The changing trend of the motor is inconsistent, and the motor is considered to be in a bad operating state, and an alarm for a bad motor operating state is issued.
8. The method for evaluating the operating status of a motor in an automated production line according to any one of claims 5 to 7, characterized in that: For the motor belt, when the actual power of the motor suddenly decreases, it is considered that the belt is slipping; when the actual power of the motor suddenly increases, it is considered that the belt is blocked.